As the number of point clouds is very large, the Transformer network used in MAE [1]} is not suitable for our case. We adopt the 3D Sparse Convolution [2]} to build the encoder network, in which positional encoding is applied to contain the positional information and improves training efficiency. As the 3D Sparse Convolution only focuses on the voxels containing point clouds, our voxel masking strategy can reduce the memory complexity for training, similar to Transformer network.
